Pharmacogenomics of Adrenal Suppression with Inhaled Corticosteroids (PhASIC) Amber Dahlin PhD MMSc, Jessica Lasky-Su ScD, Ann Wu MD MPH, Kelan Tantisira MD MPH, Scott Weiss MD MPH Project Description Recent advances in systems biology have enabled the identification of molecular predictors of treatment outcomes for complex diseases, including asthma. Asthma is one of the most common chronic diseases in the world, and inhaled corticosteroids are routinely prescribed medications for management of symptoms. While generally well tolerated, the long-term use of corticosteroids can elicit serious side effects, including adrenal suppression, an important adverse event with the potential to lead to acute adrenal crisis due to suppression of cortisol. As 30% or more of asthma patients experience poor steroid responsiveness and require increased doses to control asthma symptoms, these patients are at increased risk of developing adrenal suppression. Both corticosteroid responsiveness and adrenal suppression demonstrate repeatable inter-individual variation that is partly due to genetics; however, the genetic and molecular contributions to both steroid response and adrenal suppression are not well understood. Given the potential severity of poor responsiveness to corticosteroids, the ability of clinicians to predict how asthma patients may respond could mitigate unnecessary drug exposure and reduce the risk of side effects, including adrenal suppression. This innovation could ultimately allow clinicians to tailor treatment regimens more precisely to avoid adverse events, thereby addressing an unmet clinical need. Pharmacogenomics bears the promise to improve asthma care, and focusing on identifying the molecular ?drivers? of corticosteroid response could significantly improve treatment outcomes for patients. The goal of this study is to identify the genes and metabolites that contribute to steroid responsiveness in asthma. We will clarify the molecular drivers of steroid resistance in asthma using an innovative, systems biology-based approach that integrates genomic and metabolomic profiling of large populations with predictive computational modeling, making the discovery of biologic implications of treatment response more likely, as no single ?omics? approach is likely to lead to translatable findings. These molecular predictors could ultimately comprise a future clinical or diagnostic test to advance precision medicine efforts for asthma treatment. In addition, through this research effort, we will increase understanding of how adverse events such as adrenal suppression are linked to treatment outcomes. This work is anticipated to provide novel information to drive new treatment paradigms and, ultimately, improve patient risk profiles for asthma.